class_docstr: A state-settable environment for Tic-Tac-Toe game.
  
  For MCTS/AlphaZero, we need to keep states of the environment in a node and
  later restore them once MCTS selects which node to visit. This requires
  calling into get_state() and set_state() functions.
  
  The states are a 3 x 3 array where 0 = empty, 1 = player, 2 = opponent.
  The action is a 2-d vector to indicate the position for the player's move.
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code: def _reset(self):
      self._states = np.zeros((3, 3), np.int32)
      return TimeStep(
          StepType.FIRST,
          np.asarray(0.0, dtype=np.float32),
          self._discount,
          self._states,
      )
  
file_path: agents/tf_agents/environments/examples/tic_tac_toe_environment.py
imports: import copy
  import numpy
  from tf_agents.environments import py_environment
  from tf_agents.specs import BoundedArraySpec
  from tf_agents.trajectories.time_step import StepType
  from tf_agents.trajectories.time_step import TimeStep
label_desc: Use image node_id 4 for calling the TicTacToeEnvironment obj's underlying member method code with example usage: obj._reset() and returns: TimeStep